Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations22411
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.2 MiB
Average record size in memory1.0 KiB

Variable types

Text5
Categorical10
DateTime1
Numeric12
Boolean1
Path1

Alerts

acreage is highly overall correlated with acreage_boxcox and 1 other fieldsHigh correlation
acreage_boxcox is highly overall correlated with acreage and 1 other fieldsHigh correlation
acreage_log is highly overall correlated with acreage and 1 other fieldsHigh correlation
bedrooms is highly overall correlated with finished_area and 1 other fieldsHigh correlation
building_value is highly overall correlated with finished_area and 3 other fieldsHigh correlation
city is highly overall correlated with property_city and 1 other fieldsHigh correlation
finished_area is highly overall correlated with bedrooms and 3 other fieldsHigh correlation
full_bath is highly overall correlated with bedrooms and 2 other fieldsHigh correlation
home_age is highly overall correlated with year_builtHigh correlation
land_value is highly overall correlated with building_value and 1 other fieldsHigh correlation
neighborhood is highly overall correlated with property_city and 1 other fieldsHigh correlation
price_range is highly overall correlated with sale_priceHigh correlation
property_city is highly overall correlated with city and 2 other fieldsHigh correlation
sale_price is highly overall correlated with building_value and 3 other fieldsHigh correlation
tax_district is highly overall correlated with city and 2 other fieldsHigh correlation
year_built is highly overall correlated with home_ageHigh correlation
suite_condo___ is highly imbalanced (99.9%) Imbalance
sold_as_vacant is highly imbalanced (85.8%) Imbalance
multiple_parcels_involved_in_sale is highly imbalanced (83.6%) Imbalance
city is highly imbalanced (60.6%) Imbalance
acreage is highly skewed (γ1 = 25.71904787) Skewed

Reproduction

Analysis started2025-07-24 21:56:09.103245
Analysis finished2025-07-24 21:56:39.199472
Duration30.1 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct19480
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-07-24T16:56:39.666521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters336165
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16843 ?
Unique (%)75.2%

Sample

1st row105 11 0 080.00
2nd row118 03 0 130.00
3rd row119 01 0 479.00
4th row119 05 0 186.00
5th row119 05 0 387.00
ValueCountFrequency (%)
0 22411
25.0%
07 1680
 
1.9%
01 1558
 
1.7%
08 1537
 
1.7%
13 1497
 
1.7%
15 1485
 
1.7%
03 1448
 
1.6%
02 1412
 
1.6%
11 1411
 
1.6%
05 1349
 
1.5%
Other values (805) 53856
60.1%
2025-07-24T16:56:40.352434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 114315
34.0%
67233
20.0%
1 38328
 
11.4%
. 22411
 
6.7%
2 15485
 
4.6%
3 14903
 
4.4%
4 12232
 
3.6%
5 11047
 
3.3%
6 10792
 
3.2%
8 10291
 
3.1%
Other values (2) 19128
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 336165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 114315
34.0%
67233
20.0%
1 38328
 
11.4%
. 22411
 
6.7%
2 15485
 
4.6%
3 14903
 
4.4%
4 12232
 
3.6%
5 11047
 
3.3%
6 10792
 
3.2%
8 10291
 
3.1%
Other values (2) 19128
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 336165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 114315
34.0%
67233
20.0%
1 38328
 
11.4%
. 22411
 
6.7%
2 15485
 
4.6%
3 14903
 
4.4%
4 12232
 
3.6%
5 11047
 
3.3%
6 10792
 
3.2%
8 10291
 
3.1%
Other values (2) 19128
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 336165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 114315
34.0%
67233
20.0%
1 38328
 
11.4%
. 22411
 
6.7%
2 15485
 
4.6%
3 14903
 
4.4%
4 12232
 
3.6%
5 11047
 
3.3%
6 10792
 
3.2%
8 10291
 
3.1%
Other values (2) 19128
 
5.7%
Distinct20202
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-07-24T16:56:41.039396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length30
Median length26
Mean length16.675338
Min length10

Characters and Unicode

Total characters373711
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18172 ?
Unique (%)81.1%

Sample

1st row1802 STEWART PL
2nd row2761 ROSEDALE PL
3rd row224 PEACHTREE ST
4th row316 LUTIE ST
5th row2626 FOSTER AVE
ValueCountFrequency (%)
dr 7704
 
10.6%
ave 5799
 
8.0%
st 2821
 
3.9%
rd 1913
 
2.6%
ct 1299
 
1.8%
n 1116
 
1.5%
ln 810
 
1.1%
pl 445
 
0.6%
s 424
 
0.6%
cir 354
 
0.5%
Other values (6944) 49922
68.8%
2025-07-24T16:56:41.998400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72523
19.4%
E 24563
 
6.6%
R 23364
 
6.3%
A 20821
 
5.6%
D 17618
 
4.7%
1 16795
 
4.5%
N 14628
 
3.9%
L 13552
 
3.6%
T 13416
 
3.6%
O 13091
 
3.5%
Other values (27) 143340
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 373711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
72523
19.4%
E 24563
 
6.6%
R 23364
 
6.3%
A 20821
 
5.6%
D 17618
 
4.7%
1 16795
 
4.5%
N 14628
 
3.9%
L 13552
 
3.6%
T 13416
 
3.6%
O 13091
 
3.5%
Other values (27) 143340
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 373711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
72523
19.4%
E 24563
 
6.6%
R 23364
 
6.3%
A 20821
 
5.6%
D 17618
 
4.7%
1 16795
 
4.5%
N 14628
 
3.9%
L 13552
 
3.6%
T 13416
 
3.6%
O 13091
 
3.5%
Other values (27) 143340
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 373711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
72523
19.4%
E 24563
 
6.6%
R 23364
 
6.3%
A 20821
 
5.6%
D 17618
 
4.7%
1 16795
 
4.5%
N 14628
 
3.9%
L 13552
 
3.6%
T 13416
 
3.6%
O 13091
 
3.5%
Other values (27) 143340
38.4%

suite_condo___
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Unknown
22409 
3-B
 
1
2-A
 
1

Length

Max length7
Median length7
Mean length6.999643
Min length3

Characters and Unicode

Total characters156869
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 22409
> 99.9%
3-B 1
 
< 0.1%
2-A 1
 
< 0.1%

Length

2025-07-24T16:56:42.217415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:42.400419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unknown 22409
> 99.9%
3-b 1
 
< 0.1%
2-a 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 67227
42.9%
U 22409
 
14.3%
k 22409
 
14.3%
o 22409
 
14.3%
w 22409
 
14.3%
- 2
 
< 0.1%
3 1
 
< 0.1%
B 1
 
< 0.1%
2 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 67227
42.9%
U 22409
 
14.3%
k 22409
 
14.3%
o 22409
 
14.3%
w 22409
 
14.3%
- 2
 
< 0.1%
3 1
 
< 0.1%
B 1
 
< 0.1%
2 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 67227
42.9%
U 22409
 
14.3%
k 22409
 
14.3%
o 22409
 
14.3%
w 22409
 
14.3%
- 2
 
< 0.1%
3 1
 
< 0.1%
B 1
 
< 0.1%
2 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 67227
42.9%
U 22409
 
14.3%
k 22409
 
14.3%
o 22409
 
14.3%
w 22409
 
14.3%
- 2
 
< 0.1%
3 1
 
< 0.1%
B 1
 
< 0.1%
2 1
 
< 0.1%
A 1
 
< 0.1%

property_city
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
NASHVILLE
17393 
Other
2519 
ANTIOCH
 
1274
MADISON
 
1225

Length

Max length9
Median length9
Mean length8.3273839
Min length5

Characters and Unicode

Total characters186625
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASHVILLE
2nd rowNASHVILLE
3rd rowNASHVILLE
4th rowNASHVILLE
5th rowNASHVILLE

Common Values

ValueCountFrequency (%)
NASHVILLE 17393
77.6%
Other 2519
 
11.2%
ANTIOCH 1274
 
5.7%
MADISON 1225
 
5.5%

Length

2025-07-24T16:56:42.612062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:42.839089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nashville 17393
77.6%
other 2519
 
11.2%
antioch 1274
 
5.7%
madison 1225
 
5.5%

Most occurring characters

ValueCountFrequency (%)
L 34786
18.6%
N 19892
10.7%
A 19892
10.7%
I 19892
10.7%
H 18667
10.0%
S 18618
10.0%
V 17393
9.3%
E 17393
9.3%
O 5018
 
2.7%
t 2519
 
1.3%
Other values (7) 12555
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 34786
18.6%
N 19892
10.7%
A 19892
10.7%
I 19892
10.7%
H 18667
10.0%
S 18618
10.0%
V 17393
9.3%
E 17393
9.3%
O 5018
 
2.7%
t 2519
 
1.3%
Other values (7) 12555
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 34786
18.6%
N 19892
10.7%
A 19892
10.7%
I 19892
10.7%
H 18667
10.0%
S 18618
10.0%
V 17393
9.3%
E 17393
9.3%
O 5018
 
2.7%
t 2519
 
1.3%
Other values (7) 12555
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 34786
18.6%
N 19892
10.7%
A 19892
10.7%
I 19892
10.7%
H 18667
10.0%
S 18618
10.0%
V 17393
9.3%
E 17393
9.3%
O 5018
 
2.7%
t 2519
 
1.3%
Other values (7) 12555
 
6.7%
Distinct1044
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size350.2 KiB
Minimum2013-01-02 00:00:00
Maximum2016-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-24T16:56:43.040295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:43.287454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sale_price
Real number (ℝ)

High correlation 

Distinct3114
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213668.74
Minimum100
Maximum625000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:43.530430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile60000
Q1121000
median175000
Q3284900
95-th percentile482250
Maximum625000
Range624900
Interquartile range (IQR)163900

Descriptive statistics

Standard deviation129534.12
Coefficient of variation (CV)0.60623806
Kurtosis0.48838072
Mean213668.74
Median Absolute Deviation (MAD)69500
Skewness1.0314346
Sum4.7885301 × 109
Variance1.6779089 × 1010
MonotonicityNot monotonic
2025-07-24T16:56:43.763433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 261
 
1.2%
130000 231
 
1.0%
120000 231
 
1.0%
125000 230
 
1.0%
135000 223
 
1.0%
160000 219
 
1.0%
140000 216
 
1.0%
100000 201
 
0.9%
115000 200
 
0.9%
110000 199
 
0.9%
Other values (3104) 20200
90.1%
ValueCountFrequency (%)
100 2
< 0.1%
800 1
< 0.1%
1000 1
< 0.1%
3000 1
< 0.1%
5000 1
< 0.1%
6000 2
< 0.1%
6500 1
< 0.1%
7000 1
< 0.1%
7200 1
< 0.1%
7250 1
< 0.1%
ValueCountFrequency (%)
625000 23
0.1%
624900 1
 
< 0.1%
624255 1
 
< 0.1%
624000 1
 
< 0.1%
623000 2
 
< 0.1%
622758 1
 
< 0.1%
622740 1
 
< 0.1%
622500 1
 
< 0.1%
621200 1
 
< 0.1%
621000 1
 
< 0.1%
Distinct22204
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-07-24T16:56:44.280943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length16.000089
Min length16

Characters and Unicode

Total characters358578
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22045 ?
Unique (%)98.4%

Sample

1st row20130118-0006337
2nd row20130124-0008033
3rd row20130128-0008863
4th row20130131-0009929
5th row20130118-0006110
ValueCountFrequency (%)
20150916-0093955 7
 
< 0.1%
20130614-0061021 6
 
< 0.1%
20160204-0010954 5
 
< 0.1%
20140331-0026370 4
 
< 0.1%
20160908-0094751 4
 
< 0.1%
20160819-0087092 4
 
< 0.1%
20130708-0070104 4
 
< 0.1%
20151022-0107375 4
 
< 0.1%
20131226-0129773 4
 
< 0.1%
20140523-0044794 4
 
< 0.1%
Other values (22212) 22383
99.8%
2025-07-24T16:56:44.925541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 103691
28.9%
1 56345
15.7%
2 46238
12.9%
- 22413
 
6.3%
5 21551
 
6.0%
6 21323
 
5.9%
4 20887
 
5.8%
3 20540
 
5.7%
8 15227
 
4.2%
7 15185
 
4.2%
Other values (2) 15178
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103691
28.9%
1 56345
15.7%
2 46238
12.9%
- 22413
 
6.3%
5 21551
 
6.0%
6 21323
 
5.9%
4 20887
 
5.8%
3 20540
 
5.7%
8 15227
 
4.2%
7 15185
 
4.2%
Other values (2) 15178
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103691
28.9%
1 56345
15.7%
2 46238
12.9%
- 22413
 
6.3%
5 21551
 
6.0%
6 21323
 
5.9%
4 20887
 
5.8%
3 20540
 
5.7%
8 15227
 
4.2%
7 15185
 
4.2%
Other values (2) 15178
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103691
28.9%
1 56345
15.7%
2 46238
12.9%
- 22413
 
6.3%
5 21551
 
6.0%
6 21323
 
5.9%
4 20887
 
5.8%
3 20540
 
5.7%
8 15227
 
4.2%
7 15185
 
4.2%
Other values (2) 15178
 
4.2%

sold_as_vacant
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.0 KiB
False
21962 
True
 
449
ValueCountFrequency (%)
False 21962
98.0%
True 449
 
2.0%
2025-07-24T16:56:45.115334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

multiple_parcels_involved_in_sale
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
21872 
1
 
539

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22411
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21872
97.6%
1 539
 
2.4%

Length

2025-07-24T16:56:45.280309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:45.439034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 21872
97.6%
1 539
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 21872
97.6%
1 539
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21872
97.6%
1 539
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21872
97.6%
1 539
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21872
97.6%
1 539
 
2.4%
Distinct17866
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-07-24T16:56:45.984627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length49
Mean length24.29316
Min length6

Characters and Unicode

Total characters544434
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14696 ?
Unique (%)65.6%

Sample

1st rowSTINSON, LAURA M.
2nd rowNUNES, JARED R.
3rd rowWHITFORD, KAREN
4th rowHENDERSON, JAMES P. & LYNN P.
5th rowMILLER, JORDAN
ValueCountFrequency (%)
11120
 
11.9%
llc 1490
 
1.6%
a 1395
 
1.5%
m 1395
 
1.5%
l 1262
 
1.4%
j 966
 
1.0%
d 876
 
0.9%
r 855
 
0.9%
e 841
 
0.9%
c 753
 
0.8%
Other values (15242) 72303
77.5%
2025-07-24T16:56:46.817038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70876
 
13.0%
A 46475
 
8.5%
E 45734
 
8.4%
R 35320
 
6.5%
N 32549
 
6.0%
L 30028
 
5.5%
I 28245
 
5.2%
, 25817
 
4.7%
S 22980
 
4.2%
O 22280
 
4.1%
Other values (41) 184130
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 544434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70876
 
13.0%
A 46475
 
8.5%
E 45734
 
8.4%
R 35320
 
6.5%
N 32549
 
6.0%
L 30028
 
5.5%
I 28245
 
5.2%
, 25817
 
4.7%
S 22980
 
4.2%
O 22280
 
4.1%
Other values (41) 184130
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 544434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70876
 
13.0%
A 46475
 
8.5%
E 45734
 
8.4%
R 35320
 
6.5%
N 32549
 
6.0%
L 30028
 
5.5%
I 28245
 
5.2%
, 25817
 
4.7%
S 22980
 
4.2%
O 22280
 
4.1%
Other values (41) 184130
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 544434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70876
 
13.0%
A 46475
 
8.5%
E 45734
 
8.4%
R 35320
 
6.5%
N 32549
 
6.0%
L 30028
 
5.5%
I 28245
 
5.2%
, 25817
 
4.7%
S 22980
 
4.2%
O 22280
 
4.1%
Other values (41) 184130
33.8%
Distinct19477
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-07-24T16:56:47.502268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length30
Median length26
Mean length16.663335
Min length10

Characters and Unicode

Total characters373442
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16837 ?
Unique (%)75.1%

Sample

1st row1802 STEWART PL
2nd row2761 ROSEDALE PL
3rd row224 PEACHTREE ST
4th row316 LUTIE ST
5th row2626 FOSTER AVE
ValueCountFrequency (%)
dr 7704
 
10.6%
ave 5798
 
8.0%
st 2825
 
3.9%
rd 1916
 
2.6%
ct 1298
 
1.8%
n 1123
 
1.5%
ln 807
 
1.1%
pl 445
 
0.6%
s 422
 
0.6%
cir 354
 
0.5%
Other values (6883) 50043
68.8%
2025-07-24T16:56:48.442302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72155
19.3%
E 24563
 
6.6%
R 23366
 
6.3%
A 20836
 
5.6%
D 17620
 
4.7%
1 16803
 
4.5%
N 14619
 
3.9%
L 13559
 
3.6%
T 13420
 
3.6%
O 13089
 
3.5%
Other values (28) 143412
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 373442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
72155
19.3%
E 24563
 
6.6%
R 23366
 
6.3%
A 20836
 
5.6%
D 17620
 
4.7%
1 16803
 
4.5%
N 14619
 
3.9%
L 13559
 
3.6%
T 13420
 
3.6%
O 13089
 
3.5%
Other values (28) 143412
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 373442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
72155
19.3%
E 24563
 
6.6%
R 23366
 
6.3%
A 20836
 
5.6%
D 17620
 
4.7%
1 16803
 
4.5%
N 14619
 
3.9%
L 13559
 
3.6%
T 13420
 
3.6%
O 13089
 
3.5%
Other values (28) 143412
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 373442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
72155
19.3%
E 24563
 
6.6%
R 23366
 
6.3%
A 20836
 
5.6%
D 17620
 
4.7%
1 16803
 
4.5%
N 14619
 
3.9%
L 13559
 
3.6%
T 13420
 
3.6%
O 13089
 
3.5%
Other values (28) 143412
38.4%

city
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
NASHVILLE
17394 
ANTIOCH
 
1274
MADISON
 
1225
HERMITAGE
 
1022
OLD HICKORY
 
853
Other values (5)
 
643

Length

Max length14
Median length9
Mean length8.9581902
Min length7

Characters and Unicode

Total characters200762
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASHVILLE
2nd rowNASHVILLE
3rd rowNASHVILLE
4th rowNASHVILLE
5th rowNASHVILLE

Common Values

ValueCountFrequency (%)
NASHVILLE 17394
77.6%
ANTIOCH 1274
 
5.7%
MADISON 1225
 
5.5%
HERMITAGE 1022
 
4.6%
OLD HICKORY 853
 
3.8%
GOODLETTSVILLE 461
 
2.1%
BRENTWOOD 147
 
0.7%
WHITES CREEK 18
 
0.1%
JOELTON 11
 
< 0.1%
MOUNT JULIET 6
 
< 0.1%

Length

2025-07-24T16:56:48.662391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:48.898029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nashville 17394
74.7%
antioch 1274
 
5.5%
madison 1225
 
5.3%
hermitage 1022
 
4.4%
old 853
 
3.7%
hickory 853
 
3.7%
goodlettsville 461
 
2.0%
brentwood 147
 
0.6%
whites 18
 
0.1%
creek 18
 
0.1%
Other values (3) 23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
L 37041
18.5%
I 22253
11.1%
A 20915
10.4%
E 20578
10.2%
H 20561
10.2%
N 20057
10.0%
S 19098
9.5%
V 17855
8.9%
O 5449
 
2.7%
T 3406
 
1.7%
Other values (12) 13549
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 37041
18.5%
I 22253
11.1%
A 20915
10.4%
E 20578
10.2%
H 20561
10.2%
N 20057
10.0%
S 19098
9.5%
V 17855
8.9%
O 5449
 
2.7%
T 3406
 
1.7%
Other values (12) 13549
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 37041
18.5%
I 22253
11.1%
A 20915
10.4%
E 20578
10.2%
H 20561
10.2%
N 20057
10.0%
S 19098
9.5%
V 17855
8.9%
O 5449
 
2.7%
T 3406
 
1.7%
Other values (12) 13549
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 37041
18.5%
I 22253
11.1%
A 20915
10.4%
E 20578
10.2%
H 20561
10.2%
N 20057
10.0%
S 19098
9.5%
V 17855
8.9%
O 5449
 
2.7%
T 3406
 
1.7%
Other values (12) 13549
 
6.7%

acreage
Real number (ℝ)

High correlation  Skewed 

Distinct359
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40779349
Minimum0.04
Maximum47.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:49.155082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.12
Q10.18
median0.27
Q30.42
95-th percentile1.05
Maximum47.5
Range47.46
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.64330883
Coefficient of variation (CV)1.5775358
Kurtosis1451.5138
Mean0.40779349
Median Absolute Deviation (MAD)0.1
Skewness25.719048
Sum9139.06
Variance0.41384626
MonotonicityNot monotonic
2025-07-24T16:56:49.371233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 1613
 
7.2%
0.18 1059
 
4.7%
0.2 914
 
4.1%
0.23 859
 
3.8%
0.22 733
 
3.3%
0.19 693
 
3.1%
0.25 692
 
3.1%
0.27 692
 
3.1%
0.34 669
 
3.0%
0.16 599
 
2.7%
Other values (349) 13888
62.0%
ValueCountFrequency (%)
0.04 3
 
< 0.1%
0.05 18
 
0.1%
0.06 34
 
0.2%
0.07 50
 
0.2%
0.08 86
 
0.4%
0.09 352
1.6%
0.1 212
0.9%
0.11 285
1.3%
0.12 335
1.5%
0.13 369
1.6%
ValueCountFrequency (%)
47.5 1
< 0.1%
22.77 1
< 0.1%
19.87 1
< 0.1%
16.17 1
< 0.1%
12.87 1
< 0.1%
12.2 1
< 0.1%
11.74 1
< 0.1%
11.31 1
< 0.1%
10.94 1
< 0.1%
10.67 1
< 0.1%

tax_district
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
URBAN SERVICES DISTRICT
17340 
GENERAL SERVICES DISTRICT
4273 
Other
 
798

Length

Max length25
Median length23
Mean length22.740395
Min length5

Characters and Unicode

Total characters509635
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBAN SERVICES DISTRICT
2nd rowOther
3rd rowURBAN SERVICES DISTRICT
4th rowURBAN SERVICES DISTRICT
5th rowURBAN SERVICES DISTRICT

Common Values

ValueCountFrequency (%)
URBAN SERVICES DISTRICT 17340
77.4%
GENERAL SERVICES DISTRICT 4273
 
19.1%
Other 798
 
3.6%

Length

2025-07-24T16:56:49.587137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:49.772875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
services 21613
32.9%
district 21613
32.9%
urban 17340
26.4%
general 4273
 
6.5%
other 798
 
1.2%

Most occurring characters

ValueCountFrequency (%)
S 64839
12.7%
I 64839
12.7%
R 64839
12.7%
E 51772
10.2%
C 43226
8.5%
T 43226
8.5%
43226
8.5%
D 21613
 
4.2%
A 21613
 
4.2%
N 21613
 
4.2%
Other values (10) 68829
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 509635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 64839
12.7%
I 64839
12.7%
R 64839
12.7%
E 51772
10.2%
C 43226
8.5%
T 43226
8.5%
43226
8.5%
D 21613
 
4.2%
A 21613
 
4.2%
N 21613
 
4.2%
Other values (10) 68829
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 509635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 64839
12.7%
I 64839
12.7%
R 64839
12.7%
E 51772
10.2%
C 43226
8.5%
T 43226
8.5%
43226
8.5%
D 21613
 
4.2%
A 21613
 
4.2%
N 21613
 
4.2%
Other values (10) 68829
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 509635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 64839
12.7%
I 64839
12.7%
R 64839
12.7%
E 51772
10.2%
C 43226
8.5%
T 43226
8.5%
43226
8.5%
D 21613
 
4.2%
A 21613
 
4.2%
N 21613
 
4.2%
Other values (10) 68829
13.5%

neighborhood
Real number (ℝ)

High correlation 

Distinct190
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4367.3589
Minimum107
Maximum9530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:49.987527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile1226
Q13130
median4026
Q36228
95-th percentile7331
Maximum9530
Range9423
Interquartile range (IQR)3098

Descriptive statistics

Standard deviation2053.7244
Coefficient of variation (CV)0.47024403
Kurtosis-0.30480992
Mean4367.3589
Median Absolute Deviation (MAD)1500
Skewness0.36950668
Sum97876880
Variance4217784.1
MonotonicityNot monotonic
2025-07-24T16:56:50.218260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4026 726
 
3.2%
2326 600
 
2.7%
7328 570
 
2.5%
2026 491
 
2.2%
3628 453
 
2.0%
1226 446
 
2.0%
6226 427
 
1.9%
3426 426
 
1.9%
2726 400
 
1.8%
2328 381
 
1.7%
Other values (180) 17491
78.0%
ValueCountFrequency (%)
107 2
 
< 0.1%
126 221
1.0%
226 343
1.5%
326 38
 
0.2%
1026 61
 
0.3%
1111 1
 
< 0.1%
1113 2
 
< 0.1%
1126 182
0.8%
1127 106
 
0.5%
1129 99
 
0.4%
ValueCountFrequency (%)
9530 32
 
0.1%
9529 91
0.4%
9528 64
 
0.3%
9527 4
 
< 0.1%
9526 26
 
0.1%
9328 93
0.4%
9327 88
0.4%
9326 194
0.9%
9226 164
0.7%
9126 13
 
0.1%

image
Path

Distinct19480
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
\112000\363001.JPG
 
4
\87000\722001.JPG
 
4
\48000\188001.JPG
 
4
\36000\736001.JPG
 
4
\28000\79001.JPG
 
4
Other values (19475)
22391 

Length

Max length18
Median length17
Mean length17.297532
Min length13

Characters and Unicode

Total characters387655
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16843 ?
Unique (%)75.2%

Sample

1st row\114000\910001.JPG
2nd row\131000\191001.JPG
3rd row\133000\721001.JPG
4th row\134000\474001.JPG
5th row\134000\656001.JPG

Common Values

ValueCountFrequency (%)
\112000\363001.JPG 4
 
< 0.1%
\87000\722001.JPG 4
 
< 0.1%
\48000\188001.JPG 4
 
< 0.1%
\36000\736001.JPG 4
 
< 0.1%
\28000\79001.JPG 4
 
< 0.1%
\28000\80001.JPG 4
 
< 0.1%
\61000\253001.JPG 4
 
< 0.1%
\133000\892001.JPG 4
 
< 0.1%
\150000\78001.JPG 4
 
< 0.1%
\34000\853001.JPG 4
 
< 0.1%
Other values (19470) 22371
99.8%

Length

2025-07-24T16:56:50.432125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
112000\363001.jpg 4
 
< 0.1%
74000\502001.jpg 4
 
< 0.1%
87000\722001.jpg 4
 
< 0.1%
73000\486001.jpg 4
 
< 0.1%
73000\867001.jpg 4
 
< 0.1%
36000\112001.jpg 4
 
< 0.1%
62000\411001.jpg 4
 
< 0.1%
86000\701001.jpg 4
 
< 0.1%
47000\445001.jpg 4
 
< 0.1%
61000\560001.jpg 4
 
< 0.1%
Other values (19470) 22371
99.8%

Most occurring characters

ValueCountFrequency (%)
0 119441
30.8%
\ 44822
 
11.6%
1 41947
 
10.8%
. 22411
 
5.8%
J 22411
 
5.8%
P 22411
 
5.8%
G 22411
 
5.8%
7 12196
 
3.1%
6 11956
 
3.1%
3 11597
 
3.0%
Other values (5) 56052
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 387655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 119441
30.8%
\ 44822
 
11.6%
1 41947
 
10.8%
. 22411
 
5.8%
J 22411
 
5.8%
P 22411
 
5.8%
G 22411
 
5.8%
7 12196
 
3.1%
6 11956
 
3.1%
3 11597
 
3.0%
Other values (5) 56052
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 387655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 119441
30.8%
\ 44822
 
11.6%
1 41947
 
10.8%
. 22411
 
5.8%
J 22411
 
5.8%
P 22411
 
5.8%
G 22411
 
5.8%
7 12196
 
3.1%
6 11956
 
3.1%
3 11597
 
3.0%
Other values (5) 56052
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 387655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 119441
30.8%
\ 44822
 
11.6%
1 41947
 
10.8%
. 22411
 
5.8%
J 22411
 
5.8%
P 22411
 
5.8%
G 22411
 
5.8%
7 12196
 
3.1%
6 11956
 
3.1%
3 11597
 
3.0%
Other values (5) 56052
14.5%
Common prefix\
Unique stems19480
Unique names1000
Unique extensions1
Unique directories255
Unique anchors1
ValueCountFrequency (%)
\112000\363001.JPG 4
 
< 0.1%
\87000\722001.JPG 4
 
< 0.1%
\48000\188001.JPG 4
 
< 0.1%
\36000\736001.JPG 4
 
< 0.1%
\28000\79001.JPG 4
 
< 0.1%
\28000\80001.JPG 4
 
< 0.1%
\61000\253001.JPG 4
 
< 0.1%
\133000\892001.JPG 4
 
< 0.1%
\150000\78001.JPG 4
 
< 0.1%
\34000\853001.JPG 4
 
< 0.1%
Other values (19470) 22371
99.8%
ValueCountFrequency (%)
\112000\363001 4
 
< 0.1%
\87000\722001 4
 
< 0.1%
\48000\188001 4
 
< 0.1%
\36000\736001 4
 
< 0.1%
\28000\79001 4
 
< 0.1%
\28000\80001 4
 
< 0.1%
\61000\253001 4
 
< 0.1%
\133000\892001 4
 
< 0.1%
\150000\78001 4
 
< 0.1%
\34000\853001 4
 
< 0.1%
Other values (19470) 22371
99.8%
ValueCountFrequency (%)
941001.JPG 38
 
0.2%
511001.JPG 38
 
0.2%
722001.JPG 37
 
0.2%
477001.JPG 37
 
0.2%
650001.JPG 37
 
0.2%
739001.JPG 37
 
0.2%
189001.JPG 36
 
0.2%
560001.JPG 36
 
0.2%
446001.JPG 36
 
0.2%
35001.JPG 35
 
0.2%
Other values (990) 22044
98.4%
ValueCountFrequency (%)
.JPG 22411
100.0%
ValueCountFrequency (%)
\66000 288
 
1.3%
\73000 268
 
1.2%
\48000 267
 
1.2%
\82000 256
 
1.1%
\93000 255
 
1.1%
\36000 252
 
1.1%
\72000 249
 
1.1%
\37000 247
 
1.1%
\71000 240
 
1.1%
\67000 238
 
1.1%
Other values (245) 19851
88.6%
ValueCountFrequency (%)
22411
100.0%

land_value
Real number (ℝ)

High correlation 

Distinct724
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51785.933
Minimum900
Maximum754000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:50.650928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile13000
Q121000
median27000
Q345000
95-th percentile200000
Maximum754000
Range753100
Interquartile range (IQR)24000

Descriptive statistics

Standard deviation62264.705
Coefficient of variation (CV)1.2023479
Kurtosis9.3224644
Mean51785.933
Median Absolute Deviation (MAD)9000
Skewness2.8053702
Sum1.1605745 × 109
Variance3.8768935 × 109
MonotonicityNot monotonic
2025-07-24T16:56:50.880186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 1431
 
6.4%
26000 1015
 
4.5%
27000 872
 
3.9%
30000 805
 
3.6%
18000 758
 
3.4%
24000 729
 
3.3%
15000 613
 
2.7%
22000 611
 
2.7%
45000 597
 
2.7%
21000 555
 
2.5%
Other values (714) 14425
64.4%
ValueCountFrequency (%)
900 2
 
< 0.1%
1600 1
 
< 0.1%
3000 3
 
< 0.1%
6000 18
0.1%
6500 2
 
< 0.1%
6800 1
 
< 0.1%
7000 2
 
< 0.1%
7500 35
0.2%
8000 16
0.1%
8400 3
 
< 0.1%
ValueCountFrequency (%)
754000 1
< 0.1%
587100 1
< 0.1%
573800 2
< 0.1%
546800 2
< 0.1%
518400 1
< 0.1%
516400 1
< 0.1%
505600 1
< 0.1%
486000 2
< 0.1%
485800 1
< 0.1%
484500 1
< 0.1%

building_value
Real number (ℝ)

High correlation 

Distinct3525
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141619.78
Minimum1600
Maximum1665800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:51.118140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile46800
Q181650
median112200
Q3168800
95-th percentile310300
Maximum1665800
Range1664200
Interquartile range (IQR)87150

Descriptive statistics

Standard deviation108265.56
Coefficient of variation (CV)0.7644805
Kurtosis29.625909
Mean141619.78
Median Absolute Deviation (MAD)38300
Skewness4.1026409
Sum3.1738409 × 109
Variance1.1721431 × 1010
MonotonicityNot monotonic
2025-07-24T16:56:51.356354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88000 37
 
0.2%
70000 35
 
0.2%
86300 34
 
0.2%
102900 33
 
0.1%
98900 33
 
0.1%
81000 33
 
0.1%
90600 31
 
0.1%
102500 31
 
0.1%
88200 31
 
0.1%
83500 30
 
0.1%
Other values (3515) 22083
98.5%
ValueCountFrequency (%)
1600 1
 
< 0.1%
2300 1
 
< 0.1%
2700 1
 
< 0.1%
2900 2
< 0.1%
3300 1
 
< 0.1%
3400 2
< 0.1%
3500 1
 
< 0.1%
4000 3
< 0.1%
4200 1
 
< 0.1%
4300 1
 
< 0.1%
ValueCountFrequency (%)
1665800 2
< 0.1%
1620600 1
< 0.1%
1608000 1
< 0.1%
1442500 1
< 0.1%
1346100 1
< 0.1%
1324900 1
< 0.1%
1310500 1
< 0.1%
1284000 1
< 0.1%
1273900 2
< 0.1%
1265400 1
< 0.1%

finished_area
Real number (ℝ)

High correlation 

Distinct5095
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1734.5755
Minimum450
Maximum10788.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:51.595306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum450
5-th percentile854
Q11209
median1575
Q32073.125
95-th percentile3096.2
Maximum10788.58
Range10338.58
Interquartile range (IQR)864.125

Descriptive statistics

Standard deviation769.63364
Coefficient of variation (CV)0.44370143
Kurtosis9.8289063
Mean1734.5755
Median Absolute Deviation (MAD)419
Skewness2.1499302
Sum38873572
Variance592335.94
MonotonicityNot monotonic
2025-07-24T16:56:51.827730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 114
 
0.5%
1200 113
 
0.5%
768 112
 
0.5%
1000 102
 
0.5%
975 100
 
0.4%
1350 95
 
0.4%
1050 92
 
0.4%
1650 86
 
0.4%
960 81
 
0.4%
1500 76
 
0.3%
Other values (5085) 21440
95.7%
ValueCountFrequency (%)
450 1
 
< 0.1%
463 1
 
< 0.1%
504 1
 
< 0.1%
520 1
 
< 0.1%
528 1
 
< 0.1%
540 1
 
< 0.1%
560 1
 
< 0.1%
569 1
 
< 0.1%
576 5
< 0.1%
580 1
 
< 0.1%
ValueCountFrequency (%)
10788.58008 1
< 0.1%
10492 1
< 0.1%
10006 1
< 0.1%
9864 1
< 0.1%
9180 1
< 0.1%
9170 1
< 0.1%
9111 1
< 0.1%
8286 1
< 0.1%
8022.65999 1
< 0.1%
7770 1
< 0.1%

foundation_type
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
CRAWL
14537 
FULL BSMT
3660 
PT BSMT
2608 
SLAB
1548 
PIERS
 
31

Length

Max length9
Median length5
Mean length5.8193298
Min length4

Characters and Unicode

Total characters130417
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPT BSMT
2nd rowSLAB
3rd rowFULL BSMT
4th rowCRAWL
5th rowCRAWL

Common Values

ValueCountFrequency (%)
CRAWL 14537
64.9%
FULL BSMT 3660
 
16.3%
PT BSMT 2608
 
11.6%
SLAB 1548
 
6.9%
PIERS 31
 
0.1%
TYPICAL 27
 
0.1%

Length

2025-07-24T16:56:52.050431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:52.239133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
crawl 14537
50.7%
bsmt 6268
21.9%
full 3660
 
12.8%
pt 2608
 
9.1%
slab 1548
 
5.4%
piers 31
 
0.1%
typical 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
L 23432
18.0%
A 16112
12.4%
R 14568
11.2%
C 14564
11.2%
W 14537
11.1%
T 8903
 
6.8%
S 7847
 
6.0%
B 7816
 
6.0%
6268
 
4.8%
M 6268
 
4.8%
Other values (6) 10102
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130417
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 23432
18.0%
A 16112
12.4%
R 14568
11.2%
C 14564
11.2%
W 14537
11.1%
T 8903
 
6.8%
S 7847
 
6.0%
B 7816
 
6.0%
6268
 
4.8%
M 6268
 
4.8%
Other values (6) 10102
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130417
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 23432
18.0%
A 16112
12.4%
R 14568
11.2%
C 14564
11.2%
W 14537
11.1%
T 8903
 
6.8%
S 7847
 
6.0%
B 7816
 
6.0%
6268
 
4.8%
M 6268
 
4.8%
Other values (6) 10102
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130417
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 23432
18.0%
A 16112
12.4%
R 14568
11.2%
C 14564
11.2%
W 14537
11.1%
T 8903
 
6.8%
S 7847
 
6.0%
B 7816
 
6.0%
6268
 
4.8%
M 6268
 
4.8%
Other values (6) 10102
7.7%

year_built
Real number (ℝ)

High correlation 

Distinct124
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1963.267
Minimum1799
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:52.465070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1799
5-th percentile1925
Q11948
median1960
Q31980
95-th percentile2015
Maximum2017
Range218
Interquartile range (IQR)32

Descriptive statistics

Standard deviation25.74478
Coefficient of variation (CV)0.013113234
Kurtosis-0.2083814
Mean1963.267
Median Absolute Deviation (MAD)15
Skewness0.29181141
Sum43998777
Variance662.79367
MonotonicityNot monotonic
2025-07-24T16:56:52.984066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 1098
 
4.9%
1955 973
 
4.3%
1930 919
 
4.1%
1960 736
 
3.3%
1940 699
 
3.1%
2015 605
 
2.7%
2016 528
 
2.4%
1948 515
 
2.3%
1920 509
 
2.3%
1958 506
 
2.3%
Other values (114) 15323
68.4%
ValueCountFrequency (%)
1799 1
 
< 0.1%
1870 1
 
< 0.1%
1880 1
 
< 0.1%
1890 1
 
< 0.1%
1893 1
 
< 0.1%
1894 1
 
< 0.1%
1899 79
0.4%
1900 65
0.3%
1901 1
 
< 0.1%
1902 2
 
< 0.1%
ValueCountFrequency (%)
2017 12
 
0.1%
2016 528
2.4%
2015 605
2.7%
2014 441
2.0%
2013 238
 
1.1%
2012 48
 
0.2%
2011 19
 
0.1%
2010 25
 
0.1%
2009 30
 
0.1%
2008 45
 
0.2%

exterior_wall
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
BRICK
10901 
FRAME
8502 
BRICK/FRAME
2413 
Other
 
595

Length

Max length11
Median length5
Mean length5.646022
Min length5

Characters and Unicode

Total characters126533
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRICK
2nd rowBRICK/FRAME
3rd rowBRICK/FRAME
4th rowFRAME
5th rowFRAME

Common Values

ValueCountFrequency (%)
BRICK 10901
48.6%
FRAME 8502
37.9%
BRICK/FRAME 2413
 
10.8%
Other 595
 
2.7%

Length

2025-07-24T16:56:53.192008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:53.371988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
brick 10901
48.6%
frame 8502
37.9%
brick/frame 2413
 
10.8%
other 595
 
2.7%

Most occurring characters

ValueCountFrequency (%)
R 24229
19.1%
B 13314
10.5%
I 13314
10.5%
C 13314
10.5%
K 13314
10.5%
F 10915
8.6%
A 10915
8.6%
M 10915
8.6%
E 10915
8.6%
/ 2413
 
1.9%
Other values (5) 2975
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 24229
19.1%
B 13314
10.5%
I 13314
10.5%
C 13314
10.5%
K 13314
10.5%
F 10915
8.6%
A 10915
8.6%
M 10915
8.6%
E 10915
8.6%
/ 2413
 
1.9%
Other values (5) 2975
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 24229
19.1%
B 13314
10.5%
I 13314
10.5%
C 13314
10.5%
K 13314
10.5%
F 10915
8.6%
A 10915
8.6%
M 10915
8.6%
E 10915
8.6%
/ 2413
 
1.9%
Other values (5) 2975
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 24229
19.1%
B 13314
10.5%
I 13314
10.5%
C 13314
10.5%
K 13314
10.5%
F 10915
8.6%
A 10915
8.6%
M 10915
8.6%
E 10915
8.6%
/ 2413
 
1.9%
Other values (5) 2975
 
2.4%

grade
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
C
17033 
B
3024 
D
1959 
Other
 
395

Length

Max length5
Median length4
Mean length4.0176253
Min length4

Characters and Unicode

Total characters90039
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 17033
76.0%
B 3024
 
13.5%
D 1959
 
8.7%
Other 395
 
1.8%

Length

2025-07-24T16:56:53.565235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:53.739998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
c 17033
76.0%
b 3024
 
13.5%
d 1959
 
8.7%
other 395
 
1.8%

Most occurring characters

ValueCountFrequency (%)
66048
73.4%
C 17033
 
18.9%
B 3024
 
3.4%
D 1959
 
2.2%
O 395
 
0.4%
t 395
 
0.4%
h 395
 
0.4%
e 395
 
0.4%
r 395
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
66048
73.4%
C 17033
 
18.9%
B 3024
 
3.4%
D 1959
 
2.2%
O 395
 
0.4%
t 395
 
0.4%
h 395
 
0.4%
e 395
 
0.4%
r 395
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
66048
73.4%
C 17033
 
18.9%
B 3024
 
3.4%
D 1959
 
2.2%
O 395
 
0.4%
t 395
 
0.4%
h 395
 
0.4%
e 395
 
0.4%
r 395
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
66048
73.4%
C 17033
 
18.9%
B 3024
 
3.4%
D 1959
 
2.2%
O 395
 
0.4%
t 395
 
0.4%
h 395
 
0.4%
e 395
 
0.4%
r 395
 
0.4%

bedrooms
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0155727
Minimum0
Maximum9
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:53.918213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78721276
Coefficient of variation (CV)0.26104917
Kurtosis2.7655047
Mean3.0155727
Median Absolute Deviation (MAD)0
Skewness0.81014683
Sum67582
Variance0.61970393
MonotonicityNot monotonic
2025-07-24T16:56:54.084883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 12538
55.9%
2 5049
22.5%
4 3996
 
17.8%
5 503
 
2.2%
6 169
 
0.8%
1 100
 
0.4%
0 22
 
0.1%
8 17
 
0.1%
7 16
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 22
 
0.1%
1 100
 
0.4%
2 5049
22.5%
3 12538
55.9%
4 3996
 
17.8%
5 503
 
2.2%
6 169
 
0.8%
7 16
 
0.1%
8 17
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 17
 
0.1%
7 16
 
0.1%
6 169
 
0.8%
5 503
 
2.2%
4 3996
 
17.8%
3 12538
55.9%
2 5049
22.5%
1 100
 
0.4%
0 22
 
0.1%

full_bath
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7688189
Minimum0
Maximum7
Zeros36
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:54.241024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79980058
Coefficient of variation (CV)0.45216647
Kurtosis1.5310482
Mean1.7688189
Median Absolute Deviation (MAD)1
Skewness1.0219547
Sum39641
Variance0.63968097
MonotonicityNot monotonic
2025-07-24T16:56:54.411697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 9650
43.1%
1 9303
41.5%
3 2797
 
12.5%
4 503
 
2.2%
5 99
 
0.4%
0 36
 
0.2%
6 21
 
0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 36
 
0.2%
1 9303
41.5%
2 9650
43.1%
3 2797
 
12.5%
4 503
 
2.2%
5 99
 
0.4%
6 21
 
0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 21
 
0.1%
5 99
 
0.4%
4 503
 
2.2%
3 2797
 
12.5%
2 9650
43.1%
1 9303
41.5%
0 36
 
0.2%

half_bath
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0.0
16869 
1.0
5382 
2.0
 
160

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters67233
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16869
75.3%
1.0 5382
 
24.0%
2.0 160
 
0.7%

Length

2025-07-24T16:56:54.597551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:54.763350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16869
75.3%
1.0 5382
 
24.0%
2.0 160
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 39280
58.4%
. 22411
33.3%
1 5382
 
8.0%
2 160
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39280
58.4%
. 22411
33.3%
1 5382
 
8.0%
2 160
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39280
58.4%
. 22411
33.3%
1 5382
 
8.0%
2 160
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39280
58.4%
. 22411
33.3%
1 5382
 
8.0%
2 160
 
0.2%

acreage_log
Real number (ℝ)

High correlation 

Distinct359
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30634138
Minimum0.039220713
Maximum3.8815638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:54.971804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.039220713
5-th percentile0.11332869
Q10.16551444
median0.2390169
Q30.35065687
95-th percentile0.71783979
Maximum3.8815638
Range3.8423431
Interquartile range (IQR)0.18514243

Descriptive statistics

Standard deviation0.22849917
Coefficient of variation (CV)0.74589717
Kurtosis18.203877
Mean0.30634138
Median Absolute Deviation (MAD)0.075793839
Skewness3.1752863
Sum6865.4166
Variance0.052211869
MonotonicityNot monotonic
2025-07-24T16:56:55.219720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1570037488 1613
 
7.2%
0.1655144385 1059
 
4.7%
0.1823215568 914
 
4.1%
0.2070141694 859
 
3.8%
0.1988508587 733
 
3.3%
0.1739533071 693
 
3.1%
0.2231435513 692
 
3.1%
0.2390169005 692
 
3.1%
0.292669614 669
 
3.0%
0.1484200051 599
 
2.7%
Other values (349) 13888
62.0%
ValueCountFrequency (%)
0.03922071315 3
 
< 0.1%
0.04879016417 18
 
0.1%
0.05826890812 34
 
0.2%
0.06765864847 50
 
0.2%
0.07696104114 86
 
0.4%
0.08617769624 352
1.6%
0.0953101798 212
0.9%
0.1043600153 285
1.3%
0.1133286853 335
1.5%
0.1222176327 369
1.6%
ValueCountFrequency (%)
3.881563798 1
< 0.1%
3.168424281 1
< 0.1%
3.038312721 1
< 0.1%
2.843163675 1
< 0.1%
2.629728234 1
< 0.1%
2.58021683 1
< 0.1%
2.54474665 1
< 0.1%
2.51041194 1
< 0.1%
2.479894108 1
< 0.1%
2.457021446 1
< 0.1%

acreage_boxcox
Real number (ℝ)

High correlation 

Distinct359
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.062583828
Minimum-2.5120995
Maximum2.1239307
Zeros0
Zeros (%)0.0%
Negative12973
Negative (%)57.9%
Memory size350.2 KiB
2025-07-24T16:56:55.456482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-2.5120995
5-th percentile-1.4481346
Q1-0.8490128
median-0.17160914
Q30.57399423
95-th percentile1.6980335
Maximum2.1239307
Range4.6360301
Interquartile range (IQR)1.423007

Descriptive statistics

Standard deviation0.96424466
Coefficient of variation (CV)-15.40725
Kurtosis-0.55926397
Mean-0.062583828
Median Absolute Deviation (MAD)0.67740366
Skewness0.40631312
Sum-1402.5662
Variance0.92976776
MonotonicityNot monotonic
2025-07-24T16:56:55.680813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9393714401 1613
 
7.2%
-0.8490128003 1059
 
4.7%
-0.6783243322 914
 
4.1%
-0.4451673978 859
 
3.8%
-0.5200185959 733
 
3.3%
-0.7620485818 693
 
3.1%
-0.3034459541 692
 
3.1%
-0.1716091362 692
 
3.1%
0.2232433046 669
 
3.0%
-1.033287726 599
 
2.7%
Other values (349) 13888
62.0%
ValueCountFrequency (%)
-2.512099478 3
 
< 0.1%
-2.358622393 18
 
0.1%
-2.2116095 34
 
0.2%
-2.070731353 50
 
0.2%
-1.935678161 86
 
0.4%
-1.806158439 352
1.6%
-1.681897781 212
0.9%
-1.562637707 285
1.3%
-1.448134615 335
1.5%
-1.338158795 369
1.6%
ValueCountFrequency (%)
2.123930658 1
< 0.1%
2.12386014 1
< 0.1%
2.123815579 1
< 0.1%
2.123695851 1
< 0.1%
2.123426151 1
< 0.1%
2.123328968 1
< 0.1%
2.12324817 1
< 0.1%
2.123159745 1
< 0.1%
2.123071663 1
< 0.1%
2.122999162 1
< 0.1%

home_age
Real number (ℝ)

High correlation 

Distinct124
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.732988
Minimum8
Maximum226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size350.2 KiB
2025-07-24T16:56:55.911972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10
Q145
median65
Q377
95-th percentile100
Maximum226
Range218
Interquartile range (IQR)32

Descriptive statistics

Standard deviation25.74478
Coefficient of variation (CV)0.4170344
Kurtosis-0.2083814
Mean61.732988
Median Absolute Deviation (MAD)15
Skewness-0.29181141
Sum1383498
Variance662.79367
MonotonicityNot monotonic
2025-07-24T16:56:56.132024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 1098
 
4.9%
70 973
 
4.3%
95 919
 
4.1%
65 736
 
3.3%
85 699
 
3.1%
10 605
 
2.7%
9 528
 
2.4%
77 515
 
2.3%
105 509
 
2.3%
67 506
 
2.3%
Other values (114) 15323
68.4%
ValueCountFrequency (%)
8 12
 
0.1%
9 528
2.4%
10 605
2.7%
11 441
2.0%
12 238
 
1.1%
13 48
 
0.2%
14 19
 
0.1%
15 25
 
0.1%
16 30
 
0.1%
17 45
 
0.2%
ValueCountFrequency (%)
226 1
 
< 0.1%
155 1
 
< 0.1%
145 1
 
< 0.1%
135 1
 
< 0.1%
132 1
 
< 0.1%
131 1
 
< 0.1%
126 79
0.4%
125 65
0.3%
124 1
 
< 0.1%
123 2
 
< 0.1%

price_range
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.3 KiB
Mid
11963 
High
5854 
Low
3688 
Luxury
 
906

Length

Max length6
Median length3
Mean length3.3824907
Min length3

Characters and Unicode

Total characters75805
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid
2nd rowMid
3rd rowLow
4th rowMid
5th rowLow

Common Values

ValueCountFrequency (%)
Mid 11963
53.4%
High 5854
26.1%
Low 3688
 
16.5%
Luxury 906
 
4.0%

Length

2025-07-24T16:56:56.345492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T16:56:56.534036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
mid 11963
53.4%
high 5854
26.1%
low 3688
 
16.5%
luxury 906
 
4.0%

Most occurring characters

ValueCountFrequency (%)
i 17817
23.5%
M 11963
15.8%
d 11963
15.8%
H 5854
 
7.7%
g 5854
 
7.7%
h 5854
 
7.7%
L 4594
 
6.1%
o 3688
 
4.9%
w 3688
 
4.9%
u 1812
 
2.4%
Other values (3) 2718
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 17817
23.5%
M 11963
15.8%
d 11963
15.8%
H 5854
 
7.7%
g 5854
 
7.7%
h 5854
 
7.7%
L 4594
 
6.1%
o 3688
 
4.9%
w 3688
 
4.9%
u 1812
 
2.4%
Other values (3) 2718
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 17817
23.5%
M 11963
15.8%
d 11963
15.8%
H 5854
 
7.7%
g 5854
 
7.7%
h 5854
 
7.7%
L 4594
 
6.1%
o 3688
 
4.9%
w 3688
 
4.9%
u 1812
 
2.4%
Other values (3) 2718
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 17817
23.5%
M 11963
15.8%
d 11963
15.8%
H 5854
 
7.7%
g 5854
 
7.7%
h 5854
 
7.7%
L 4594
 
6.1%
o 3688
 
4.9%
w 3688
 
4.9%
u 1812
 
2.4%
Other values (3) 2718
 
3.6%

Interactions

2025-07-24T16:56:35.923207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:14.761787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.695328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.623741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.465481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:22.461820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:24.550394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:26.386235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:28.222534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:30.230411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:32.139421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:34.122095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.080125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:14.930786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.847881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.784860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.636593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:22.629635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:24.711531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:26.542867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:28.377496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:30.395561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:32.305371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:34.294285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.218520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:15.079663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.984809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.931476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.818419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:22.783571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:24.854155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:26.686427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:28.542678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:30.543800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:32.469636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:34.430434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.365204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:15.232776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:17.131000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:19.078495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.985276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:22.946402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.002475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:26.842999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:28.687346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:30.698281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:32.634803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:34.581327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.526739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:15.399080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:17.293029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:19.243742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:21.160791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:23.275565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.169381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.004705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:28.849385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:30.869437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:32.816287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:34.741646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.687295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:15.599828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:17.447798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:19.407006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:21.340548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:23.438619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.330005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.162898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:29.013378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.044293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:32.988286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:34.900469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.833230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:15.755236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:17.727650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:19.558773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:21.502491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:23.595363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.478356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.311556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:29.163532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.201502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:33.150235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:35.047259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:36.975523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:15.907738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:17.865927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:19.705562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:21.655151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:23.745999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.622629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.455298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:29.303977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.349232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:33.305644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:35.190321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:37.111374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.054818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.006557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:19.847358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:21.804600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:23.895183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.767406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.598654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:29.439525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.498843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:33.462368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:35.325649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:37.270468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.225258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.158915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.009730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:21.977519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:24.060383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:25.928520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.760501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:29.592425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.662908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:33.633462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:35.481587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:37.442452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.398999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.321733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.178576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:22.155440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:24.240677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:26.098378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:27.932801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:29.948667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.836415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:33.811377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:35.647749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:37.585956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:16.548807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:18.456716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:20.322855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:22.309579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:24.395469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:26.243693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:28.080701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:30.092534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:31.983677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:33.965198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-24T16:56:35.784179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-24T16:56:56.701310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
acreageacreage_boxcoxacreage_logbedroomsbuilding_valuecityexterior_wallfinished_areafoundation_typefull_bathgradehalf_bathhome_ageland_valuemultiple_parcels_involved_in_saleneighborhoodprice_rangeproperty_citysale_pricesold_as_vacantsuite_condo___tax_districtyear_built
acreage1.0001.0001.0000.1630.1740.1470.0230.3200.0570.0830.0280.005-0.0300.3050.0690.4600.0260.0360.2070.0000.0000.0590.030
acreage_boxcox1.0001.0001.0000.1630.1740.1140.2870.3200.1030.0830.2280.079-0.0300.3050.0890.4600.1900.1460.2070.0930.0130.2370.030
acreage_log1.0001.0001.0000.1630.1740.1350.1650.3200.0870.0830.1480.029-0.0300.3050.0830.4600.1320.0960.2070.0200.0000.2100.030
bedrooms0.1630.1630.1631.0000.3980.0580.1080.5880.3760.5240.2440.144-0.2000.2280.0640.0560.1750.0900.2700.0920.0000.0820.200
building_value0.1740.1740.1740.3981.0000.0840.0810.7440.0840.5480.4920.252-0.0990.6180.000-0.0730.3480.1270.6750.2630.0000.1510.099
city0.1470.1140.1350.0580.0841.0000.1640.0620.0900.0500.1740.0560.1820.0770.0660.4260.1821.0000.1210.0370.0550.7240.182
exterior_wall0.0230.2870.1650.1080.0810.1641.0000.1150.1840.1040.1770.0340.3980.1070.0480.2760.1080.1500.1210.1100.0000.0920.398
finished_area0.3200.3200.3200.5880.7440.0620.1151.0000.2320.6460.4400.251-0.2000.4890.0600.0660.2960.0570.5390.1900.0000.1410.200
foundation_type0.0570.1030.0870.3760.0840.0900.1840.2321.0000.3910.1780.0460.1510.1050.0620.1180.1480.0980.1250.0490.0000.0570.151
full_bath0.0830.0830.0830.5240.5480.0500.1040.6460.3911.0000.3590.136-0.3070.3170.0480.0290.2230.0420.3730.1700.0000.1060.307
grade0.0280.2280.1480.2440.4920.1740.1770.4400.1780.3591.0000.1990.2800.2750.0610.2610.2900.1080.3170.2230.0040.1330.280
half_bath0.0050.0790.0290.1440.2520.0560.0340.2510.0460.1360.1991.0000.1960.1010.0000.0870.0910.0430.1050.1440.0000.0370.196
home_age-0.030-0.030-0.030-0.200-0.0990.1820.398-0.2000.151-0.3070.2800.1961.0000.1030.080-0.2010.1630.2490.0900.3560.0000.205-1.000
land_value0.3050.3050.3050.2280.6180.0770.1070.4890.1050.3170.2750.1010.1031.0000.038-0.0260.3860.1090.7270.0830.0130.304-0.103
multiple_parcels_involved_in_sale0.0690.0890.0830.0640.0000.0660.0480.0600.0620.0480.0610.0000.0800.0381.0000.0750.0180.0610.0290.1070.0000.0330.080
neighborhood0.4600.4600.4600.056-0.0730.4260.2760.0660.1180.0290.2610.087-0.201-0.0260.0751.0000.2420.570-0.1000.0850.0000.8480.201
price_range0.0260.1900.1320.1750.3480.1820.1080.2960.1480.2230.2900.0910.1630.3860.0180.2421.0000.1610.9250.1410.0000.1750.163
property_city0.0360.1460.0960.0900.1271.0000.1500.0570.0980.0420.1080.0430.2490.1090.0610.5700.1611.0000.1890.0360.0070.5820.249
sale_price0.2070.2070.2070.2700.6750.1210.1210.5390.1250.3730.3170.1050.0900.7270.029-0.1000.9250.1891.0000.1960.0000.196-0.090
sold_as_vacant0.0000.0930.0200.0920.2630.0370.1100.1900.0490.1700.2230.1440.3560.0830.1070.0850.1410.0360.1961.0000.0000.0400.356
suite_condo___0.0000.0130.0000.0000.0000.0550.0000.0000.0000.0000.0040.0000.0000.0130.0000.0000.0000.0070.0000.0001.0000.0030.000
tax_district0.0590.2370.2100.0820.1510.7240.0920.1410.0570.1060.1330.0370.2050.3040.0330.8480.1750.5820.1960.0400.0031.0000.205
year_built0.0300.0300.0300.2000.0990.1820.3980.2000.1510.3070.2800.196-1.000-0.1030.0800.2010.1630.249-0.0900.3560.0000.2051.000

Missing values

2025-07-24T16:56:38.107002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-24T16:56:38.853474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

parcel_idproperty_addresssuite_condo___property_citysale_datesale_pricelegal_referencesold_as_vacantmultiple_parcels_involved_in_saleowner_nameaddresscityacreagetax_districtneighborhoodimageland_valuebuilding_valuefinished_areafoundation_typeyear_builtexterior_wallgradebedroomsfull_bathhalf_bathacreage_logacreage_boxcoxhome_ageprice_range
0105 11 0 080.001802 STEWART PLUnknownNASHVILLE2013-01-1119150020130118-0006337No0STINSON, LAURA M.1802 STEWART PLNASHVILLE0.17URBAN SERVICES DISTRICT3127.0\114000\910001.JPG32000.0134400.01149.00000PT BSMT1941.0BRICKC2.01.00.00.157004-0.93937184.0Mid
1118 03 0 130.002761 ROSEDALE PLUnknownNASHVILLE2013-01-1820200020130124-0008033No0NUNES, JARED R.2761 ROSEDALE PLNASHVILLE0.11Other9126.0\131000\191001.JPG34000.0157800.02090.82495SLAB2000.0BRICK/FRAMEC3.02.01.00.104360-1.56263825.0Mid
2119 01 0 479.00224 PEACHTREE STUnknownNASHVILLE2013-01-183200020130128-0008863No0WHITFORD, KAREN224 PEACHTREE STNASHVILLE0.17URBAN SERVICES DISTRICT3130.0\133000\721001.JPG25000.0243700.02145.60001FULL BSMT1948.0BRICK/FRAMEB4.02.00.00.157004-0.93937177.0Low
3119 05 0 186.00316 LUTIE STUnknownNASHVILLE2013-01-2310200020130131-0009929No0HENDERSON, JAMES P. & LYNN P.316 LUTIE STNASHVILLE0.34URBAN SERVICES DISTRICT3130.0\134000\474001.JPG25000.0138100.01969.00000CRAWL1910.0FRAMEC2.01.00.00.2926700.223243115.0Mid
4119 05 0 387.002626 FOSTER AVEUnknownNASHVILLE2013-01-049373620130118-0006110No0MILLER, JORDAN2626 FOSTER AVENASHVILLE0.17URBAN SERVICES DISTRICT3130.0\134000\656001.JPG25000.086100.01037.00000CRAWL1945.0FRAMEC2.01.00.00.157004-0.93937180.0Low
5119 13 0 183.00501 MORTON AVEUnknownNASHVILLE2013-01-154400020130115-0004888No0MICKLER, PATRICK L. & LOIS J. & ARNETT, RYAN D.501 MORTON AVENASHVILLE0.20URBAN SERVICES DISTRICT3179.0\136000\266001.JPG16000.068100.01216.00000CRAWL1932.0FRAMED2.01.00.00.182322-0.67832493.0Low
6119 13 0 183.00501 MORTON AVEUnknownNASHVILLE2013-01-254990020130128-0008950No0MICKLER, PATRICK L. & LOIS J. & ARNETT, RYAN D.501 MORTON AVENASHVILLE0.20URBAN SERVICES DISTRICT3179.0\136000\266001.JPG16000.068100.01216.00000CRAWL1932.0FRAMED2.01.00.00.182322-0.67832493.0Low
7119 15 0 158.00113 NEESE DRUnknownNASHVILLE2013-01-092500020130111-0003850No0SONA LAND CO, LLC113 NEESE DRNASHVILLE0.40URBAN SERVICES DISTRICT3131.0\137000\81001.JPG25000.057100.01152.00000CRAWL1945.0FRAMEC2.01.00.00.3364720.49468780.0Low
8133 07 0 195.00184 WHEELER AVEUnknownNASHVILLE2013-01-189000020130123-0007357No0GEOGHEGAN, SARS ELIZABETH184 WHEELER AVENASHVILLE0.34URBAN SERVICES DISTRICT3131.0\150000\856001.JPG25000.080100.01300.00000CRAWL1955.0BRICKC2.01.00.00.2926700.22324370.0Low
9133 12 0 153.00238 ELYSIAN FIELDS RDUnknownNASHVILLE2013-01-117200020130115-0004796No0ROSS, BRANDON & LUTTRELL, ELLEN J.238 ELYSIAN FIELDS RDNASHVILLE0.23URBAN SERVICES DISTRICT3926.0\151000\699001.JPG21500.087900.01175.00000CRAWL1968.0BRICKC3.01.01.00.207014-0.44516757.0Low
parcel_idproperty_addresssuite_condo___property_citysale_datesale_pricelegal_referencesold_as_vacantmultiple_parcels_involved_in_saleowner_nameaddresscityacreagetax_districtneighborhoodimageland_valuebuilding_valuefinished_areafoundation_typeyear_builtexterior_wallgradebedroomsfull_bathhalf_bathacreage_logacreage_boxcoxhome_ageprice_range
24107150 11 0 105.003441 NEW TOWNE RDUnknownANTIOCH2016-10-0613900020161013-0108154No0ELLER, WILLIAM R. JR.3441 NEW TOWNE RDANTIOCH0.23URBAN SERVICES DISTRICT6027.0\179000\841001.JPG27500.077500.01500.00CRAWL1983.0FRAMEC3.01.01.00.207014-0.44516742.0Mid
24108150 13 0 002.003548 ROUNDWOOD FOREST DRUnknownANTIOCH2016-10-0515000020161024-0112189No0LAO, LAI KUAN3548 ROUNDWOOD FOREST DRANTIOCH0.26URBAN SERVICES DISTRICT6028.0\180000\311001.JPG20400.089000.01201.00CRAWL1990.0BRICK/FRAMEC3.02.00.00.231112-0.23634635.0Mid
24109163 01 0 014.004240 KEVINWOOD CTUnknownANTIOCH2016-10-146770020161019-0110710No0TOVAR, SUNEM4240 KEVINWOOD CTANTIOCH0.49URBAN SERVICES DISTRICT4271.0\193000\80001.JPG22500.095600.01486.75CRAWL1999.0FRAMEC3.03.00.00.3987760.81536926.0Low
24110164 13 0 102.00425 ASHEFORD CTUnknownANTIOCH2016-10-1319480020161017-0109394No0EMUJAKPORUE, LINDA425 ASHEFORD CTANTIOCH0.23URBAN SERVICES DISTRICT6333.0\193000\840001.JPG27500.0102600.01680.00SLAB1997.0FRAMEC3.02.00.00.207014-0.44516728.0Mid
24111165 13 0 021.00904 STONEVIEW CTUnknownANTIOCH2016-10-3116500020161104-0116771No0CARRANZA, JOSE ANTONIO LOPEZ904 STONEVIEW CTANTIOCH0.24URBAN SERVICES DISTRICT6328.0\194000\62001.JPG22000.074500.01311.00SLAB1992.0FRAMEC3.02.00.00.215111-0.37301633.0Mid
24112176 01 0 003.004617 ROCKLAND TRLUnknownANTIOCH2016-10-1318500020161019-0110290No0GOODWIN, BENJAMIN DAVIS & SAMUEL GORDON DAVIS4617 ROCKLAND TRLANTIOCH0.38URBAN SERVICES DISTRICT6328.0\199000\483001.JPG25000.0105000.01758.00CRAWL1996.0BRICK/FRAMEC3.02.00.00.3220830.41009129.0Mid
24113176 05 0 070.005004 SUNSHINE DRUnknownANTIOCH2016-10-2621400020161102-0115842No0FREO TENNESSEE, LLC5004 SUNSHINE DRANTIOCH0.27URBAN SERVICES DISTRICT6328.0\199000\727001.JPG25000.0142400.02421.00SLAB1996.0BRICK/FRAMEC3.03.00.00.239017-0.17160929.0Mid
24114176 09 0 003.004964 HICKORY WOODS EUnknownANTIOCH2016-10-2823600020161031-0114817No0CHHAY, CHOWAN & NIM, PHALLY4964 HICKORY WOODS EANTIOCH0.23URBAN SERVICES DISTRICT6328.0\200000\85001.JPG25000.0159300.03117.00SLAB1995.0BRICK/FRAMEC3.03.00.00.207014-0.44516730.0Mid
24115082 05 0 040.001625 5TH AVE NUnknownNASHVILLE2016-10-2846600020161102-0115988No0GLAUS, WILLIAM D. SR.1625 5TH AVE NNASHVILLE0.15URBAN SERVICES DISTRICT126.0\66000\843001.JPG40000.0204100.01637.00CRAWL2004.0FRAMEB3.02.01.00.139762-1.13093421.0High
24117082 05 0 098.001709 3RD AVE NUnknownNASHVILLE2016-10-1228000020161017-0109149No0Unknown1709 3RD AVE NNASHVILLE0.20URBAN SERVICES DISTRICT126.0\66000\896001.JPG40000.05100.01180.00CRAWL1899.0BRICKD3.01.00.00.182322-0.678324126.0High